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Our \"stakeholder synergy\" perspective identifies new value creation opportunities that are especially effective strategically because a single strategic action (1) increases different types of value for two or more essential stakeholder groups simultaneously, and (2) does not reduce the value already received by any other essential stakeholder group. This result is obtainable because multiple potential sources of value creation exist for each essential stakeholder group. Actions that meet these criteria increase the size of the value \"pie\" available for essential stakeholder groups, and thereby serve to attract exceptional stakeholders and obtain their increasing effort and commitment. The stakeholder synergy perspective extends stakeholder theory further into the strategy realm, and offers insights for realizing broader value creation that is more likely to produce sustainable competitive advantage.

This paper examines how utility functions perform in tackling the multicriteria decision-making problem, especially one-switch utility function. Linear-times-exponential one-switch, exponential, and linear utility functions are implemented, which transforms corresponding criteria into utilities with ELECTRE I method. The detailed formulation of the decision model is presented. A numerical example about environmental policy selection is introduced to illustrate the use of the new decision model. With different wealth levels and utility functions for a policymaker, the inconsistent outranking policies illustrate the special characteristic of linear-times-exponential one-switch utility function whose initial wealth level has a significant impact on the outranking environmental policy. This study is also the first study applying one-switch utility function in address/ing multicriteria decision-making problem.

We formulate and carry out an analytical treatment of a single-period portfolio choice model featuring a reference point in wealth, S-shaped utility (value) functions with loss aversion, and probability weighting under Kahneman and Tversky's cumulative prospect theory (CPT). We introduce a new measure of loss aversion for large payoffs, called the large-loss aversion degree (LLAD), and show that it is a critical determinant of the well-posedness of the model. The sensitivity of the CPT value function with respect to the stock allocation is then investigated, which, as a by-product, demonstrates that this function is neither concave nor convex. We finally derive optimal solutions explicitly for the cases in which the reference point is the risk-free return and those in which it is not (while the utility function is piecewise linear), and we employ these results to investigate comparative statics of optimal risky exposures with respect to the reference point, the LLAD, and the curvature of the probability weighting. This paper was accepted by Wei Xiong, finance.

Any utility function that is unbounded either from below or from above implies paradoxical behavior. However, these paradoxes may be regarded as irrelevant if they involve wealth levels that are realistically meaningless. Employing real-world constraints on wealth reveals that CRRA utility with relative risk aversion outside of the range 0.75–1.15 yields paradoxical choices that very few individuals, if any, would ever make. Thus, relative risk aversion must be close to 1, the value corresponding to log preferences. These results shed new light on the longstanding debate about the geometric-mean criterion and the argument of stocks for the long-run.

Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distributionF if he or she issues the probabilistic forecast F, rather than G ≠ F. It is strictly proper if the maximum is unique. In prediction problems, proper scoring rules encourage the forecaster to make careful assessments and to be honest. In estimation problems, strictly proper scoring rules provide attractive loss and utility functions that can be tailored to the problem at hand. This article reviews and develops the theory of proper scoring rules on general probability spaces, and proposes and discusses examples thereof. Proper scoring rules derive from convex functions and relate to information measures, entropy functions, and Bregman divergences. In the case of categorical variables, we prove a rigorous version of the Savage representation. Examples of scoring rules for probabilistic forecasts in the form of predictive densities include the logarithmic, spherical, pseudospherical, and quadratic scores. The continuous ranked probability score applies to probabilistic forecasts that take the form of predictive cumulative distribution functions. It generalizes the absolute error and forms a special case of a new and very general type of score, the energy score. Like many other scoring rules, the energy score admits a kernel representation in terms of negative definite functions, with links to inequalities of Hoeffding type, in both univariate and multivariate settings. Proper scoring rules for quantile and interval forecasts are also discussed. We relate proper scoring rules to Bayes factors and to cross-validation, and propose a novel form of cross-validation known as random-fold cross-validation. A case study on probabilistic weather forecasts in the North American Pacific Northwest illustrates the importance of propriety. We note optimum score approaches to point and quantile estimation, and propose the intuitively appealing interval score as a utility function in interval estimation that addresses width as well as coverage.

The currently available transport modeling tools are used to evaluate the effects of behavior change. The aim of this study is to analyze the interaction between the transport mode choice and travel behavior of an individual—more specifically, to identify which of the variables has the greatest effect on mode choice. This is realized by using a multinomial logit model (MNL) and a nested logit model (NL) based on a utility function. The utility function contains activity characteristics, trip characteristics including travel cost, travel time, the distance between activity place, and the individual characteristics to calculate the maximum utility of the mode choice. The variables in the proposed model are tested by using real observations in Budapest, Hungary as a case study. When analyzing the results, it was found that “Trip distance” variable was the most significant, followed by “Travel time” and “Activity purpose”. These parameters have to be mainly considered when elaborating urban traffic models and travel plans. The advantage of using the proposed logit models and utility function is the ability to identify the relationship among the travel behavior of an individual and the mode choice. With the results, it is possible to estimate the influence of the various variables on mode choice and identify the best mode based on the utility function.

As a distributing center for passengers, the subway station directly affects the entire subway system’s safe operation. Accurate operational subway station risk evaluation has an important significance in risk avoidance and accident emergency response. By analyzing the application of interval type-2 fuzzy TOPSIS (IT2-FTOPSIS) method in risk evaluation, the existing research lacks consideration of the utility function and cannot reflect the actual operational risk of the subway station. To overcome these shortcomings, we develop an IT2-FTOPSIS approach with a utility function and utilize it to evaluate the subway station’s operational risk. Finally, the example of Beijing Subway is selected to illustrate the developed risk evaluation approach’s performance. The results show that the same event has different effects on subway station safe operation at different times or spaces. Namely, the same event may have different risk utility values in different situations. Thus, the developed IT2-FTOPSIS model with a utility function can improve the risk evaluation’s accuracy and reflect the subway station’s operational risk state more reasonable than the previous method.

Economic modeling of behavior under uncertainty has almost exclusively used one of two approaches. First comes the mean–variance tradeoff, central to the financial economics literature. The alternative approach uses a specific function to assess expected utility. In this approach, almost all of the economics literature has used either the exponential utility (EU) function, with constant absolute risk aversion (CARA) or the power utility (PU) function with constant relative risk aversion (CRRA). Using a Taylor Series expansion, I show that higher-order terms (skewness and kurtosis) can significantly affect estimates of expected utility. I provide specific formulaic guidance allowing economists to assess when these terms become important. This guidance uses readily observable parameters such as mean, median and variance in a wide array of non-Gaussian statistical distributions. I next review two generalizations, one for CRRA utility, hyperbolic absolute risk aversion (HARA), and one for CARA utility, exponential power (EP). I then introduce the possibility of risk-seeking behavior, both in standard economic theory and in the “value” portion of prospect theory (PT), and provide a new Generalized Logistic Utility (GLU) that automatically incorporates such behavior in its functional form. Next, I introduce issues involved in modeling utility that includes a non-marketable component such as health, the environment, or altruism, and discuss how the choice of utility function alters these models. I then assess the choice between exact utility functions and Taylor Series approximations. I conclude by discussing methods to estimate utility function parameters and methods to choose among alternative estimates.

We propose a novel multi-objective reinforcement learning algorithm that successfully learns the optimal policy even for non-linear utility functions. Non-linear utility functions pose a challenge for SOTA approaches, both in terms of learning efficiency as well as the solution concept. A key insight is that, by proposing a critic that learns a multi-variate distribution over the returns, which is then combined with accumulated rewards, we can directly optimize on the utility function, even if it is non-linear. This allows us to vastly increase the range of problems that can be solved compared to those which can be handled by single-objective methods or multi-objective methods requiring linear utility functions, yet avoiding the need to learn the full Pareto front. We demonstrate our method on multiple multi-objective benchmarks, and show that it learns effectively where baseline approaches fail.

This study introduces the Choquet integral of fuzzifying functions with respect to a fuzzy measure. To express various phenomena or ambiguous values in many applications may not be enough to show them as a function, in which case a fuzzifying function can be applied to achieve better expressions or flexibility for given function values. To apply Choquet integrals of fuzzifying functions, we consider Choquet integrals of interval-valued functions as an operator which are α-level functions of fuzzifying functions. In this study, we investigate some properties of Choquet integral of fuzzifying functions and present their applications. As part of this, a series of relevant examples and their subsequent applications are provided, along with the fuzzification of two integrands: a probability density function (PDF) and a utility function.